American Economic Review: Papers & Proceedings 2013, 103(3): 362–368
http://dx.doi.org/10.1257/aer.103.3.362
Behavioral Biases and Firm Behavior:
Evidence from Kenyan Retail Shops †
By Michael Kremer, Jean Lee, Jonathan Robinson, and Olga Rostapshova*
Many subjects in lab experiments show considerable risk aversion in small-stakes gambles.1
This is counter to the predictions of expected
utility theory for any reasonable degree of risk
aversion (Rabin 2000) but is consistent with loss
aversion in prospect theory. Benjamin, Brown,
and Shapiro (forthcoming) show that math skills
reduce small-stakes risk aversion, consistent with
broader evidence that mathematical skills can
help debias decision making (Burks et al. 2007).
In this paper, we show that acceptance of
small risky gambles and scores on math tests is
associated with inventory accumulation among
Kenyan shopkeepers. More broadly, we argue
that loss aversion may be one factor helping
explain the broader puzzle of why high rates of
return on capital among small firms in developing countries are not arbitraged away and do not
lead to the high growth rates of consumption
that the Euler equation would predict.
The development literature has documented
that, across a wide range of contexts, small
b­usiness owners in developing countries often
leave profitable investments unexploited.2 This
seems to be true not only for large, lumpy investments, such as investments in machinery that
might expose firm owners to substantial risk,
but also for divisible investments where standard
dynamic optimization theory would not predict
poverty traps, and expected utility theory would
suggest that risk should play a relatively small
role in investment decisions. For example, many
farmers fail to invest in any fertilizer despite
apparently large returns and despite the availability of fertilizer in small quantities (Duflo, Kremer,
and Robinson 2011).3
In Kremer et al. (2013) we show that many
Kenyan shopkeepers fail to make small inventory investments with high expected returns. In
this paper, we examine the determinants of inventory investments and show that shopkeepers who
invest one standard deviation more into a risky
asset in a laboratory-style game have 10–16 percent larger inventories. Consistent with the view
that math skills may be useful in debiasing, those
with one–standard deviation higher math scores
have 14 –18 percent larger inventory levels.
Section I provides background, while Section II
reports on credit constraints and departures from
standard models of entrepreneurial decision making as determinants of inventory investment.
Section III discusses broader implications, arguing that loss aversion may be an important part
of the puzzle of why many small businesses in
developing countries do not take advantage of
high expected return investment opportunities.
In particular, we argue that loss aversion may
* Kremer: Harvard University, 1050 Massachusetts
Ave., Cambridge, MA 02139 and NBER, Littauer Center,
Cambridge, MA 02138 (e-mail: [email protected].
edu); Lee: The World Bank, 1818 H St. NW, Washington,
DC 20433 (e-mail: [email protected]); Robinson:
University of California, Santa Cruz, 457 Engineering
2, Santa Cruz, CA 95064 (e-mail: [email protected]);
Rostapshova: Harvard Kennedy School, 1050 Massachusetts
Ave., Cambridge, MA 02139 (e-mail: olga_rostapshova@
hksphd.harvard.edu). We thank Isaac Mbiti and David
Laibson for helpful comments. Abdulla Al-Sabah, Kenzo
Asahi, Pia Basurto, Dan Bjorkegren, Conner Brannen,
Elliott Collins, Sefira Fialkoff, Katie Hubner, Eva Kaplan,
Anthony Keats, Jamie McCasland, and Russell Weinstein
provided excellent research assistance. We gratefully
acknowledge funding from the SEVEN Foundation, the
Kauffman Foundation, and the NBER Africa program. We
thank IPA-Kenya for administrative support.
†
To view additional materials, and author disclosure
statement(s),visit the article page at
http://dx.doi.org/10.1257/aer.103.3.362.
1
See, for example, Tversky and Kahneman (1991), Kahneman, Knetsch, and Thaler (1990), and Thaler et al. (1997).
2
See, for example, de Mel, McKenzie, and Woodruff
(2008), McKenzie and Woodruff (2008), Fafchamps et al.
(2011), Udry and Anagol (2006), and Banerjee and Duflo
(2012).
3
Of course, in this example, returns to fertilizer are positively correlated with returns on overall agricultural output,
so this could be considered a relatively high beta investment.
362
VOL. 103 NO. 3
behavioral biases and firm behavior: evidence from Kenyan Retail shops
help explain: (i) why capital injections to small
businesses yield high estimated returns, and why
they are neither spent down over time nor generate further capital accumulation (as some poverty trap stories would suggest); (ii) why many
firm owners do not take advantage of microcredit
despite high unrealized returns to investment;
(iii) why many of those who do borrow do not
invest in their businesses; (iv) why weather insurance programs stimulate investment by farmers;
and (v) why business training based on simple
heuristics can induce firm owners to change their
behavior.
I. Background and Data
We study small-scale owner-operated rural
Kenyan retail shops selling fast moving consumer goods (FMCG). The FMCG manufacturing industry is highly concentrated, with
manufacturers setting retail prices and a single
supplier holding a very high market share in
most goods. These shops are typically located in
clusters in market centers serving the surrounding rural population, with several competing
shops in proximity.
Three features of the industry and the environment restrict the ability of firm owners who
maximize expected profits to displace competitors whose decision making departs from
expected profit maximization. First, rural shops
face competition from only a limited number
of competitors, since customers will walk only
a limited distance. Second, manufacturers fix
retail prices, precluding price competition that
could allow well-stocked, high-traffic shops to
lower prices, driving out competitors. Third,
whether due to labor market imperfections,
moral hazard with employees, regulatory issues,
or other factors, owners who manage inventory
optimally are not able to manage multiple shops
in different locations. A potential policy implication of these factors is that prohibiting retail
price maintenance could allow efficient retailers
to displace less efficient ones. With a smaller
number of larger retailers, it might also be easier for new manufacturers to enter the market
because building a distribution network would
be a less daunting barrier to entry.
In a companion paper (Kremer et al. 2013)
we use administrative data from a distributor to calculate bounds on returns to inventory.
The distributor offers progressively greater
363
bulk purchase discounts for purchases above
a set of thresholds. While many purchases are
just above the thresholds, there is a considerable mass below each threshold as well, and we
find that the median shop misses at least some
opportunities to earn rates of return in excess of
100 percent (e.g., by increasing one purchase
slightly to meet the bulk discount threshold and
correspondingly reducing the next purchase).
Returns are highly heterogeneous across firms.
That paper also calculates bounds on rates of
return to holding marginally greater inventories
of phone cards based on a survey of lost sales
due to stockouts. This method likely yields
much looser bounds since it does not factor in
the long-run costs of losing customers due to
stockouts, but nonetheless it still suggests that
more than 18 percent of firms still have rates of
return over 50 percent per year to an additional
unit of phone card inventory, based only on the
short-run sales lost to stockouts. The correlation
of bounds on returns across products is low, suggesting that shopkeepers may not be equating
the marginal return to inventories across items.
This paper examines the determinants of
inventory investment based on a survey of
shop owners served by the distributor within a
particular geographic area in Western Kenya.
Background surveys included standard demographic questions, as well as questions on the
shop owner’s access to savings and credit; ownership of land, durable goods, and other assets;
transfers given and received, other sources of
income, and self-reported credit constraints. The
survey included vocabulary and reading tests in
English and Swahili, a math problem solving
test, a digit recall memory test, Raven progressive matrices, and a maze completion speed test.
Respondents were also asked to divide a portfolio of 100 Ksh (approximately $1.33) between
a safe asset and a risky asset that paid zero with
50 percent probability and 2.5 times the amount
invested with 50 percent probability.
We collected inventory data for a subset of
380 of these shops, approximately 1.5–2 years
after the background surveys. We asked respondents to estimate the total value of their inventory (at both wholesale and retail prices) with
the enumerator’s assistance. In addition, the
respondent, together with the enumerator, calculated the value of the 13 most common items
stocked by shops. We follow de Mel, McKenzie,
and Woodruff (2008) in measuring profits by
364
MAY 2013
AEA PAPERS AND PROCEEDINGS
Table 1—Summary Statistics
Quantiles
Inventories, profits, and credit to customers
Total inventory
Inventory in top 13 items
Profits in past month
Gives out credit to customers
Amount given out in credit in past month
Background characteristics
Years of education
Years shop open
Male
Married
Age
Can read and write (Swahili)
Small stakes risk aversion
Percentage invested in risky asset
Asset ownership and formal sector income
Owner or spouse has a formal sector job
Acres land owned
Value of durable goods and animals owned
Financial access
Has bank account
Participates in ROSCA
Would you like to borrow more money but
are unable to get it (percentage “yes”)
Mean
(1)
SD
(2)
26.52
9.42
2.39
0.9
1.12
29.67
11.34
2.70
—
5.75
10.80
7.47
0.56
0.79
33.36
0.97
50th
(4)
75th
(5)
90th
(6)
8.00
2.80
0.80
—
0.10
15.00
5.16
1.20
—
0.20
30.00
10.81
3.00
—
0.50
70.00
24.93
5.60
—
1.50
3.33
5.60
—
—
9.48
8.00
3.16
—
—
27.00
11.50
6.41
—
—
32.00
12.00
9.90
—
—
38.00
16.00
14.56
—
—
46.00
0.56
0.20
0.50
0.50
0.70
0.80
0.13
1.95
11.70
—
2.64
15.32
—
0.00
4.20
—
1.00
7.00
—
2.50
11.91
—
4.50
25.50
—
—
—
—
—
—
0.83
0.42
0.37
—
—
—
25th
(3)
—
—
—
—
—
—
Notes: All monetary values in 10,000 Kenyan shillings. Exchange rate was roughly 75 Ksh to $1. There are 380 shops in the
sample.
asking respondents to report their income less
expenses and wages to other employees over the
previous 30 days.4 This question was included
only in the latter part of the data collection, so
we have profit data for 188 firms.
Summary statistics are shown in Table 1.
Shopkeepers are substantially more educated
than the typical rural resident in the area. About
13 percent of owners or their spouses have
formal sector jobs. In addition, 82 percent of
shopkeepers in our sample have bank accounts,
73 percent own land, and 42 percent participate
in a merry-go-round cooperative (ROSCA).
Inventory value and income distributions
among shopkeepers are skewed, but even at the
twenty-fifth percentile shopkeepers have high
4
This measure of profits thus includes returns to owner
labor and family labor.
incomes and wealth in an area where typical
agricultural wages are approximately $1 a day.
On average, shopkeepers invested just over
half of the portfolio in the risky asset. Onethird invested exactly one-half of the assets in
the portfolio, consistent with the behavior of
many US workers who have retirement plans
that allow them to divide their assets between
multiple investments and follow the “1/n rule”
heuristic of dividing assets evenly across all
options (Benartzi and Thaler 2001, 2007). The
mass at the 50-50 division suggests that not all
subjects are expected utility maximizers.
II. Credit Constraints, Entrepreneurial Decision
Making, and Inventory Investment
Table 2 reports regressions of inventories on
indicators of credit constraints as well as factors that could cause entrepreneurial ­decision
VOL. 103 NO. 3
behavioral biases and firm behavior: evidence from Kenyan Retail shops
365
Table 2—Correlates of Inventories and Profits
log total
inventory
(1)
(2)
log inventory on
top 13 products
(3)
(4)
Background characteristics
Years of education
−0.06
−0.09
(tens of years)
(0.20)
(0.20)
Years shop open
0.16
0.12
(tens of years)
(0.10)
(0.10)
Age
−0.01
−0.02
(0.01)** (0.01)***
0.04
(0.21)
0.20
(0.11)*
−0.02
(0.01)***
−0.01
(0.21)
0.17
(0.11)
−0.02
(0.01)***
Cognitive measures
Math score
0.18
(standardized)
(0.06)***
Raven’s matrix
0.07
(standardized)
(0.07)
Digit recall
−0.02
(standardized)
(0.07)
Seconds to finish mazes
0.03
(standardized)
(0.07)
Combined language
0.02
score (standardized)
(0.07)
0.14
0.14
(0.06)** (0.06)**
0.15
0.13
(0.07)** (0.07)**
−0.04
−0.05
(0.07)
(0.07)
0.03
0.03
(0.07)
(0.07)
0.01
0.03
(0.07)
(0.07)
0.18
(0.06)***
0.05
(0.06)
−0.03
(0.07)
0.03
(0.07)
0.04
(0.07)
Small-stakes risk aversion
Percentage invested in
0.79
0.81
risky asset
(0.23)*** (0.23)***
(out of 100 Ksh)
Asset ownership, and formal sector income
Owner or spouse has
0.00
−0.04
a formal sector job
(0.18)
(0.17)
0.03
0.07
log (acres land
owned + 1)
(0.08)
(0.08)
0.25
0.22
log (value of durable
goods and animals
(0.13)*
(0.13)*
owned + 1)
(in 10,000 Ksh)
Financial access
Has bank account
0.51
(0.24)**
−0.15
(0.18)
0.02
(0.09)
0.21
(0.13)*
0.19
(0.13)
−0.48
(0.12)***
0.00
(0.11)
Participates in ROSCA
Would like to borrow
more money but is
unable to get it
log total inventory
0.54
(0.24)**
−0.12
(0.18)
0.05
(0.09)
0.17
(0.13)
log profits in past month
(5)
(6)
(8)
0.17
0.12
(0.27)
(0.28)
0.06
0.08
(0.14)
(0.14)
−0.02
−0.02
(0.01)** (0.01)*
0.29
(0.21)
−0.02
(0.11)
−0.01
(0.01)
0.23
(0.22)
−0.05
(0.12)
−0.01
(0.01)
0.32
(0.09)***
−0.13
(0.09)
0.13
(0.09)
0.15
(0.09)
0.08
(0.09)
0.32
(0.09)***
−0.13
(0.09)
0.11
(0.09)
0.15
(0.09)
0.11
(0.09)
0.15
(0.07)**
−0.07
(0.07)
0.00
(0.07)
0.10
(0.07)
0.06
(0.07)
0.22
(0.07)***
−0.16
(0.07)**
−0.02
(0.07)
0.07
(0.07)
0.09
(0.07)
0.79
(0.32)**
0.80
(0.32)**
0.21
(0.24)
0.29
(0.26)
0.17
(0.24)
0.07
(0.11)
0.16
(0.16)
0.18
(0.24)
0.10
(0.11)
0.11
(0.17)
0.15
(0.18)
0.07
(0.08)
0.01
(0.13)
0.27
(0.19)
0.06
(0.09)
0.05
(0.13)
0.25
(0.18)
−0.20
(0.16)
−0.13
(0.15)
−0.03
(0.14)
0.08
(0.12)
−0.13
(0.11)
−0.10
(0.15)
0.01
(0.13)
−0.09
(0.12)
0.27
(0.14)**
−0.50
(0.12)***
−0.02
(0.12)
0.61
(0.06)***
log inventory on
top 13 items
Mean of dependent
variable
SD of dependent
variable
Observations
R2
(7)
0.56
(0.06)***
11.97
11.97
10.89
10.89
9.62
9.62
9.61
9.61
1.06
1.06
1.09
1.09
1.00
1.00
0.97
0.97
380
0.10
380
0.14
380
0.09
380
0.14
188
0.17
188
0.19
184
0.53
184
0.47
Notes: Dependent variables in (log) Kenyan shillings. To avoid dropping observations, we create dummy variables for
having missing information for a given variable and code the underlying variable as a 0 when it is missing.
Regressions also include controls for gender, marital status, and literacy. Standard errors in parentheses.
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
366
AEA PAPERS AND PROCEEDINGS
­ aking to depart from the predictions of stanm
dard economic models. The dependent variable
in columns 1 and 2 of Table 2 is log total inventory, while the dependent variable in columns 3
and 4 is log inventory of the top 13 items (both in
Kenyan shillings). We start with a sparse specification, including only covariates that are most
plausibly exogenous to business performance.
In the second specification, we add various measures of financial access, asset ownership, and
formal sector income. The general pattern of
results is robust to the specific list of included
covariates.
There is some evidence that could be interpreted as indicating that credit constraints affect
inventories. Shopkeepers with higher levels of
other assets have bigger inventories. However,
there is no significant correlation between inventories and self-reported credit constraints, land
ownership, or formal sector employment. There
is some evidence that those with bank accounts
have greater inventories, while members of
Rotating Savings and Credit Associations, or
ROSCAs, have smaller inventories. It is difficult
to interpret this as a causal effect of ROSCA
membership, but this could indicate lower
inventories among those who have more trouble
saving or are more subject to “taxes” on saving from family members and therefore turn to
ROSCAs.
With or without the credit constraint variables in the regression, there is evidence that
small-stakes loss aversion is associated with
lower inventories. Shopkeepers who invested
10 percent less of a 100 Ksh portfolio in an asset
yielding a 0 percent return for sure and 10 percent more in the risky asset had 7.9–8.1 percent
greater inventories. Note that a movement of
10 Ksh is equivalent to about 0.014 percent of
the value of the median respondent’s stock of
animal and consumer durables, or to 0.007 percent of the median respondent’s inventory.
A one–standard deviation increase in the math
score is robustly associated with 14–18 percent
higher inventories (columns 1 and 3, Table 2).5
The estimated effect is not affected by including
measures of credit constraints (columns 2 and 4,
5
The math test was adapted from standard psychometric and personnel IQ tests, including the Wonderlic Test and
Cognitive Reflection Test. All modules were refined for the
local context and were extensively pretested.
MAY 2013
Table 2). Raven’s matrix scores are significant in
some specifications.
Of course, it is possible that the decision
of how much to allocate to a risky portfolio is
endogenous to business performance, and that
difficulty in their business causes shopkeepers
to invest less in the risky asset. However, since
stakes are small, expected utility maximizers
should still take a positive expected value, zerobeta gamble. Moreover, reverse causality would
not explain the math results, since math scores
are largely determined by education that antedates establishment of the business.
Shopkeepers who invest more in the risky
asset have higher profits. The data are consistent
with this working through the inventory channel (see columns 5–8, Table 2). A one–standard
deviation increase in the math score is associated
with 32 percent higher profits, unconditional on
inventories. Columns 7 and 8 suggest that much
of the math score effect on profits works through
inventories, but that other channels also play a
role.6
Several factors suggest entrepreneurial decision making is not an immutable inherent characteristic. Math scores are highly correlated with
educational attainment. Kremer et al. (2013)
find that the more shopkeepers are interviewed
about stockouts, the less they stockout, and present some evidence that shopkeepers increase
purchase size after receiving information on
profits lost by missing bulk discount thresholds.
Similarly, Beaman, Magruder, and Robinson
(2012) report evidence that regularly surveying small business owners about lost sales from
inadequate supplies of small change and providing information on the lost sales costs reduces
lost sales as they increase their supply of change.
Firm owners’ willingness to change their behavior in response to interventions implies they
were not perfectly optimizing initially.
6
Note that unlike Benjamin, Brown, and Shapiro
(forthcoming) we do not find that higher math scores are
associated with lower small-stakes risk aversion in a labexperiment type game, but we do find that higher math
scores are associated with higher inventories. This may be
because the small-stakes lab experiment gambles are highly
artificial in our context, while inventory investment is a decision that shopkeepers confront every week and have had
more opportunity and incentive to think through.
VOL. 103 NO. 3
behavioral biases and firm behavior: evidence from Kenyan Retail shops
III. Discussion
Loss aversion can potentially help explain a
series of puzzles related to the persistence of
unrealized high-return investment opportunities.
Since a loss-averse firm owner may turn down
small, highly positive expected return investments if they carry risk, loss aversion offers a
potential explanation for several puzzles and
recent empirical findings: (i) why shop owners
with high unrealized returns to divisible investments do not have the high growth rates of
consumption and assets predicted by the Euler
equation even for credit-constrained agents;
(ii) why many small business owners do not
take up microcredit and why many of those
who do borrow do not use the loans for business investment;7 (iii) the finding in de Mel,
McKenzie, and Woodruff (2008) that when
small business owners are given an infusion of
new capital, they neither spend it down (as they
would if they were simply impatient) nor do they
break out of a poverty trap and accumulate more
assets; (iv) recent findings that insuring farmers against adverse weather shocks can increase
their willingness to invest (i.e., Karlan et al.
2012; Cole, Giné, and Vickery 2011; Mobarak
and Rosenzweig 2012); and (v) recent literature
on business training that suggests that training
based on “rules of thumb” or simple heuristics
can induce firm owners to change their behavior
(e.g., Drexler, Fischer, and Schoar 2012).
Our findings that small business owners
behave as if they are loss averse raise the possibility that social safety nets might increase
investment among small business owners more
generally. Our work also suggests that at least
some of the heterogeneity in returns to capital
identified by Hsieh and Klenow (2009) may be
due to differences in management quality across
firms, as opposed to the impact of tax and regulatory distortion across firms.
References
Attanasio, Orazio, Britta Augsburg, Ralph De
Haas, Emla Fitzsimons, and Heike Harmgart.
2011. “Group Lending or Individual Lending?
See Banerjee et al. (2010), Karlan and Zinman (2010),
Crépon et al. (2011), and Attanasio et al. (2011) for some
recent evidence on the effects of expanding microcredit
access.
7
367
Evidence from a Randomised Field Experiment in Mongolia.” Institute for Fiscal Studies,
IFS Working Paper W11/20.
Banerjee, Abhijit, Esther Duflo, Rachel Glennerster, and Cynthia Kinnan. 2010. “The Miracle
of Microfinance? Evidence from a Randomized Evaluation.” Unpublished.
Banerjee, Abhijit V., and Esther Duflo. 2012. “Do
Firms Want to Borrow More? Testing Credit
Constraints Using a Directed Lending Program.” Unpublished.
Beaman, Lori, Jeremy Magruder, and Jonathan
Robinson. 2012. “Minding Small Change:
Limited Attention among Small Firms in
Kenya.” Unpublished.
Benartzi, Shlomo, and Richard H. Thaler. 2007.
“Heuristics and Biases in Retirement Savings
Behavior.” Journal of Economic Perspectives
21 (3): 81–104.
Benartzi, Shlomo, and Richard H. Thaler. 2001.
“Naive Diversification Strategies in Defined
Contribution Saving Plans.” American Economic Review 91 (1): 79–98.
Benjamin, Daniel J., Sebastian A. Brown, and Jesse
M. Shapiro. Forthcoming. “Who is ‘Behav-
ioral’? Cognitive Ability and Anomalous Preferences.” Journal of the European Economic
Association.
Burks, Stephen V., Jeffrey Carpenter, Lorenz
Götte, Kristen Monaco, Kay Porter, and Aldo
Rustichini. 2007. “Using Behavioral Eco-
nomic Field Experiments at a Firm: the Context and Design of the Truckers and Turnover
Project.” In The Analysis of Firms and Employees: Quantitative and Qualitative Approaches,
edited by Stefan Bender, Julia Lane, Kathryn Shaw, Fredrik Andersson, and Till von
Wachter, 45–106. Chicago: University of Chicago Press.
Cole, Shawn, Xavier Giné, and James Vickery.
2011. “How Does Risk Management Influence
Production Decisions? Evidence from a Field
Experiment.” Unpublished.
Crépon, Bruno, Florencia Devoto, Esther Duflo,
and William Parienté. 2011. “Impact of
Microcredit in Rural Areas of Morocco:
Evidence from a Randomized Evaluation.”
Unpublished.
de Mel, Suresh, David McKenzie, and Christopher
Woodruff. 2008. “Returns to Capital in Micro-
enterprises: Evidence from a Field Experiment.” Quarterly Journal of Economics 123
(4): 1329–72.
368
AEA PAPERS AND PROCEEDINGS
MAY 2013
Drexler, Alejandro, Greg Fischer, and Antoinette
Schoar. 2012. “Keeping it Simple: Financial
“Expanding Microenterprise Credit Access:
Using Randomized Supply Decisions to Estimate the Impacts in Manila.” Unpublished.
Duflo, Esther, Michael Kremer, and Jonathan
Robinson. 2011. “Nudging Farmers to Use
Kremer, Michael, Jean Lee, Jonathan Robinson,
and Olga Rostapshova. 2013. “The Return to
Literacy and Rules of Thumb.” Unpublished.
Fertilizer: Theory and Experimental Evidence
from Kenya.” American Economic Review 101
(6): 2350–90.
Fafchamps, Marcel, David McKenzie, Simon
R. Quinn, and Christopher Woodruff. 2011.
“When is Capital Enough to get Female
Microenterprises Growing? Evidence from a
Randomized Experiment in Ghana.” National
Bureau of Economic Research Working Paper
17207.
Hsieh, Chang-Tai, and Peter J. Klenow. 2009.
“Misallocation and Manufacturing TFP in
China and India.” Quarterly Journal of Economics 124 (4): 1403–48.
Kahneman, Daniel, Jack L. Knetsch, and Richard
H. Thaler. 1990. “Experimental Tests of the
Endowment Effect and the Coase Theorem.”
Journal of Political Economy 98 (6): 1325–48.
Karlan, Dean, Robert Darko Osei, Isaac OseiAkoto, and Christopher Udry. 2012. “Agricul-
tural Decisions after Relaxing Credit and Risk
Constraints?” National Bureau of Economic
Research Working Paper 18463.
Karlan, Dean, and Jonathan Zinman. 2010.
Capital for Small Retailers in Kenya: Evidence
from Inventories.” Unpublished.
McKenzie, David, and Christopher Woodruff.
2008. “Experimental Evidence on Returns to
Capital and Access to Finance in Mexico.”
World Bank Economic Review 22 (3): 457–82.
Mobarak, A. Mushfiq, and Mark Rosenzweig.
2012. “Selling Formal Insurance to the Informally Insured.” Unpublished.
Rabin, Matthew. 2000. “Risk Aversion and
Expected-Utility Theory: A Calibration Theorem.” Econometrica 68 (5): 1281–92.
Thaler, Richard H., Amos Tversky, Daniel Kahneman, and Alan Schwartz. 1997. “The Effect of
Myopia and Loss Aversion on Risk Taking: An
Experimental Test.” Quarterly Journal of Economics 112 (2): 647–61.
Tversky, Amos, and Daniel Kahneman. 1991.
“Loss Aversion in Riskless Choice: A Reference-Dependent Model.” Quarterly Journal of
Economics 106 (4): 1039–61.
Udry, Christopher and Santosh Anagol. 2006.
“The Return to Capital in Ghana.” American
Economic Review 96 (2): 388–93.